• Respond to incoming requests made via web, email, or phone within agreed-upon levels in customer service level agreements. • Identify and organize tickets and internal initiatives according to priority. • Update and maintain internal process documentation. • Configure, maintain and troubleshoot corporate environments and applications to support Verndale's employees and clients. • Provide technical support and training for colleagues. • Collaborate with engineering, leadership, and cross-functional team members to meet business goals. • Participate in on-call rotation and ensure uptime of services.

• Support the VP of Technology Engineering & Innovation in evaluating emerging AI infrastructure technologies and future-ready data center strategies. • Analyze AI workload characteristics including: Training vs. inference workloads, GPU utilization patterns, Dynamic workload fluctuations, Rack-level power variability, Networking and latency requirements. • Assess implications of AI workload behavior on infrastructure resiliency, scalability, efficiency, and operational design. • Develop technical recommendations and infrastructure strategies supporting future AI deployments. • Analyze current and future AI compute platforms including NVIDIA GPU architectures, ARM-based platforms, custom AI accelerators and ASICs, optical networking and switching technologies, and emerging hyperscaler-designed AI chips. • Evaluate implications of evolving chip architectures on rack density, power consumption, cooling requirements, electrical distribution, mechanical infrastructure, space planning, and future development standards. • Model current and future AI rack power density trends including existing high-density deployments (50–120 kW), near-term AI deployments (150–300+ kW), and future ultra-dense AI cluster scenarios. • Assess long-term impacts of emerging chip architectures on energy efficiency and future data center design and development standards. • Support conceptual and detailed design efforts for AI-ready data center infrastructure. • Assist in developing long-term infrastructure roadmaps for high-density AI deployments, liquid cooling adoption, modular infrastructure strategies, utility coordination, grid-parallel and microgrid solutions, and future AI campus development. • Evaluate implications of AI infrastructure evolution on greenfield developments, existing facility retrofits, construction methodologies, scalability, and future campus master planning. • Collaborate with engineering, development, and construction teams to develop scalable AI-ready infrastructure standards and deployment models. • Collaborate closely with the Energy Strategy Team to evaluate utility constraints, interconnection requirements, grid limitations, dynamic load fluctuation impacts, power quality and resiliency considerations, and onsite generation and distributed energy solutions. • Support analysis of grid-parallel and islanded microgrid architectures, fuel cells, Battery Energy Storage Systems (BESS), bridge power solutions, natural gas generation, and renewable integration opportunities. • Evaluate implications of AI workloads on substation development, transmission planning, utility coordination, and energy efficiency and PUE optimization. • Assess how future AI compute growth will influence utility planning and power infrastructure strategies. • Analyze current and emerging thermal management solutions including air cooling, direct-to-chip liquid cooling, immersion cooling, rear-door heat exchangers, and hybrid cooling architectures. • Assess implications of ultra-high-density AI deployments on mechanical system design, water usage, cooling scalability, heat rejection strategies, thermal resiliency, and future cooling infrastructure standards. • Evaluate cooling technologies and infrastructure requirements as AI rack densities continue to increase. • Interface directly with technology vendors, OEMs, utilities, and strategic partners across power generation, UPS systems, electrical infrastructure, cooling technologies, liquid cooling platforms, AI compute infrastructure, and networking and optical interconnect technologies. • Lead technical assessments of next-generation technologies with respect to reliability, scalability, energy efficiency, sustainability, AI workload performance, construction complexity, and operational resiliency. • Support proof-of-concept initiatives, pilot deployments, and technology benchmarking efforts. • Develop executive-level recommendations regarding adoption of emerging AI infrastructure technologies and strategic engineering standards. • Collaborate with design engineering, construction, operations, energy strategy, procurement, utilities, technology partners, and external engineering firms and consultants. • Support strategic planning initiatives and executive-level technical presentations. • Assist in developing future infrastructure standards and innovation roadmaps for AI-enabled data center platforms.

• The IT Infrastructure Engineer is responsible for the deployment, configuration and ongoing support of enterprise infrastructure across both virtual and physical environments. • The role focuses on Windows Server deployments, clustering technologies, storage integration (MPIO), identity and access solutions (PingFederate, UserLock MFA), and application onboarding within a secure, highly available environment. • Deploy and configure Windows Server environments across VMware vCenter (virtual builds) and physical servers. • Perform system provisioning including OS installation, patching and baseline security hardening. • Manage virtual machine lifecycle (provisioning, resizing, decommissioning). • Monitor and optimise resource utilisation across clusters. • Document onboarding processes and standards. • Maintain technical documentation including SOPs and runbooks. • Administer and maintain VMware vSphere / vCenter environments. • Design and implement Windows Failover Clustering solutions. • Configure and support Multipath I/O (MPIO) for SAN storage. • Ensure resilience and high availability of critical systems. • Lead/support application onboarding including authentication integration and infrastructure provisioning. • Collaborate with application owners to meet security and connectivity requirements. • Provide support for infrastructure incidents and requests. • Troubleshoot complex issues across Windows, VMware and identity platforms. • Participate in on-call support where required. • Ensure systems meet organisational security and compliance standards. • Support patching cycles and vulnerability remediation. • Work with cyber and compliance teams as required. • Contribute to continuous service improvement initiatives.

Role Description At Nscale, our AI Infrastructure Operations team is responsible for the reliability and scalability of one of the most demanding AI platforms in the industry. We value engineers who think in systems, lead through influence, and raise the bar for operational excellence across the organisation. We’re looking for a Principal Site Reliability Engineer (SRE) to provide technical leadership across our AI Infrastructure Operations domain. This is a senior, highly impactful role focused on setting reliability strategy, designing foundational systems, and driving cross-team improvements at scale. You will operate as a technical authority for reliability, automation, and operational architecture across Nscale’s GPU, network, and control-plane platforms. - Owning and evolving the long-term reliability strategy for Nscale’s AI and HPC infrastructure - Designing and leading the development of large-scale control-plane systems, automation frameworks, and operational tooling - Defining reliability standards, SLO frameworks, and operational best practices used across multiple teams - Acting as a senior technical escalation point during critical incidents, guiding resolution and ensuring systemic fixes - Identifying structural reliability risks and driving cross-functional initiatives to address them at the architectural level - Partnering with Engineering, Network Operations, and Fleet Operations leadership to influence platform design and operational maturity - Mentoring senior and mid-level engineers, raising the overall quality and effectiveness of SRE practices - Driving measurable improvements in availability, MTTR, cost efficiency, and operational scalability Qualifications - 10+ years of experience in Site Reliability Engineering, Systems Engineering, or Software Engineering roles operating complex, large-scale infrastructure - Expert-level software engineering skills, with a strong track record of building production-grade automation and systems - Deep expertise in Linux, networking, and distributed systems design at scale - Extensive experience debugging and resolving failures across hardware, OS, networking, and application layers - Proven ability to lead technical initiatives across teams without direct authority - Strong systems-thinking mindset, with the ability to balance reliability, velocity, and cost Requirements - Deep hands-on experience with AI or HPC platforms, including GPUs, high-speed interconnects (InfiniBand/RDMA), and workload schedulers (e.g. SLURM) - Experience designing observability systems for high-cardinality, high-throughput environments - Familiarity with Kubernetes at scale and hybrid or bare-metal cloud architectures - A history of driving step-change improvements in reliability, scalability, or operational efficiency Benefits - Collaborative, supportive, and innovative environment where your contributions spark real impact - Highly competitive package (base + equity) with reviews every 12 months - Opportunity to join the fastest-growing tech startup, pushing boundaries and collaborating with brilliant minds - Dynamic progression plan tailored to your ambitions - Human-First Flexibility: autonomy to shape your day around life's moments - Thriving remote-first team with seamless virtual collaboration